Surveillance of a particular environment, such as a vehicle roadway, using a series of successive cameras includes many inherent problems. Frequently, successive cameras, referred to as camera pairs, are “non-overlapping,” meaning that the areas viewed or monitored by each camera does not overlap with the subsequent camera in the series. An example non-overlapping camera environment may be a complex road network with stationary cameras fixed along the road network, wherein the number of cameras is minimized to provide large area coverage, and hence the cameras do not overlap. As such, for a given environment, there are “gaps” or portion of the environment which are not monitored by a camera. In these cases, a vehicle passes through a viewable range of a first camera and is tracked. The vehicle then proceeds into a gap, or non-monitored area. The vehicle then enters the viewable range of the second camera (the next successive camera in the series). For certain applications, it is critical to track and identify a vehicle as it spans the distance covered by the two cameras. As such, the image or images of that vehicle identified by the first camera must be matched with the image or images of the same vehicle identified by the second camera. For example, identifying that the vehicle captured by the first camera is the same as the vehicle captured by the second camera may allow law enforcement agencies or other government entities to determine the rate of speed of the vehicle and/or the direction that vehicle is traveling. In addition, vehicle identification may be used for wide-area freeway traffic surveillance and control, specifically to measure link travel time (i.e., the actual time taken for traffic to travel between two fixed points on the freeway network), and to track the total number of vehicles traveling between any two points on the network in a given time interval.
Another conventional approach to computing the probability that two vehicle observations across two cameras are derived from the same vehicle or two different vehicles involves aligning and matching oriented edge images of pairs of vehicles across two cameras on the basis of which the same-different probabilities are learned. However, given the variations in appearances and aspect of the same vehicle across disparate observations (i.e., cameras having different surroundings and environmental influences), direct matching according to this approach may not consistently provide a reliable means of computing same-different probabilities.
Other exemplary approaches rely on direct object matching and feature learning to identify and track objects between multiple cameras. See e.g., “Bayesian multi-camera surveillance” by V. Kettnaker et al., published in Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR99) (1999) and “Tracking many objects with many sensors” by Hanna Pasula et al., published in International Joint Conferences on Artificial Intelligence, pages 1160-1171 (IJ-CA199) (1999).
However, directly matching vehicle objects between a pair of non-overlapping cameras can be very difficult due to drastic environmental differences between the two cameras, such as illumination, appearance and aspect changes.
Therefore, there is a need for a method and a system for efficiently and effectively matching a vehicle across non-overlapping camera pairs, without inter-camera matching.